从粗粒度数据多尺度重建加权网络
Multiscale Reconstruction of Weighted Networks from Coarse-Grained Data
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中文总结 AI 辅助
针对粗粒度数据重建细尺度加权网络的问题，提出一种保持函数形式的多尺度概率模型，无需重新拟合即可跨层传递参数，并在国际贸易和荷兰生产网络中验证了高精度重建能力。
中文摘要 AI 辅助
从部分信息进行网络重建通常在约束可观测的相同分辨率水平上进行。当仅有粗粒度信息可用，而相关过程发生在更细尺度时，这就会成为问题。在此，我们采用配套论文中引入的加权网络多尺度模型，并将其转化为一个概率框架，用于在任意聚合水平上重建加权网络。该模型的构建旨在保持其在粗粒化下的函数形式，从而使得在可观测聚合层上校准的全局参数可以无需重新拟合而转移到更细层。我们在两个具有不同聚合机制的经验系统上测试了该方法。在国际贸易网络中，国家被聚合为地理宏观区域，并利用观测到的粗粒度层来推断底层的国家层面网络。在荷兰生产网络中，部门流跨越层级工业分类进行重建，利用较粗的部门层来推断较细的部门层。在地理和部门两种情境下，我们将我们真正的多尺度重建方法与直接在目标分辨率下校准的最先进的加权重建模型进行基准比较，该模型因此利用了在评估性能的同一（更细）尺度上可用的额外信息。值得注意的是，尽管存在这种信息劣势，我们的方法仍以高精度恢复了细尺度二元结构，提高了精确度、特异性、准确性和最大度误差诊断，同时在敏感性上几乎保持等效。
英文摘要
Network reconstruction from partial information is usually performed at the same resolution level at which constraints are observable. This becomes problematic when only coarse-grained information is available, while the relevant process occurs at a finer scale. Here we employ the multiscale model of weighted networks introduced in a companion paper and turn it into a probabilistic framework for reconstructing weighted networks across arbitrary aggregation levels. The model is built to preserve its functional form under coarse-graining, so that global parameters calibrated on an observable aggregate layer can be transferred to finer layers without refitting. We test the method on two empirical systems with different aggregation mechanisms. In the International Trade Network, countries are aggregated into geographic macro-regions and the observed coarse-grained layer is used to infer the underlying country-level network. In the Dutch production network, sectoral flows are reconstructed across the hierarchical industrial classification, using coarser sectoral layers to infer finer ones. In both geographical and sectoral settings, we benchmark our genuinely multiscale reconstruction method against a state-of-the-art weighted reconstruction model calibrated directly at the target resolution, and therefore using additional information available at the same (finer) scale at which performance is evaluated. Remarkably, despite this informational disadvantage, our method recovers the fine-scale binary structure with high accuracy, improving precision, specificity, accuracy and maximum degree-error diagnostics, while remaining nearly equivalent in sensitivity.
发表机构
- IMT School for Advanced Studies（IMT高等研究院）
- Lorentz Institute for Theoretical Physics, University of Leiden（莱顿大学洛伦兹理论物理研究所）
- Statistics Netherlands（荷兰统计局）
- INdAM-GNAMPA Istituto Nazionale di Alta Matematica ‘Francesco Severi’（意大利高等数学国家研究所‘弗朗切斯科·塞韦里’）
- Korteweg - de Vries Institute for Mathematics, University of Amsterdam（阿姆斯特丹大学科特韦格-德弗里斯数学研究所）
机构由 AI 辅助整理，请以论文原文为准。
